When rolling a stock to a desired thickness, in general two or more rolling passes are used to obtain the thickness of the rolled material close to the desired thickness. At this time, a target value of the delivery thickness at each pass is given and the rolling force, rolling torque, and other rolling load at each pass when achieving this are predicted. Furthermore, it is becoming necessary to estimate the mill stretch, roll deflection, and other elastic deformation amounts of the rolling mill based on these predicted values and set the roll gaps and crown control amounts so as to compensate for these and to estimate the power and set the rolling speed so that these satisfy allowable ranges, then perform the rolling.
At this time, a prediction formula using the components, dimensions, temperature, rolling conditions, etc. of the stock as parameters is used so as to predict the rolling load, but error in prediction of the rolling load sometimes occurs due to the low precision of the prediction formula used and error between the settings (predicted values) of the parameters inputted into the prediction formula and the actual values. For this reason, so-called “inter-pass learning” has been performed using prediction error of the rolling load in an already performed rolling pass to correct the predicted values of rolling load for subsequent rolling passes.
As the most general inter-pass learning method, there is the method of using a prediction error rate of rolling load at a previous pass (actual pass) to set a learning coefficient CF of rolling force prediction for a rolling pass of the stock to be performed from then on (predicted pass).
For example, if considering the rolling force as the rolling load, the ratio CP between the actual value of the rolling force Pexp at an actual pass for the stock and the predicted value Pcal of the rolling force at a rolling force model for that actual pass is considered as an indicator of the prediction error of the rolling force at an actual pass (hereinafter referred to as the “prediction error rate”).
                              C          P                =                              P            exp                                P            cal                                              (        1        )            
In this regard, in general, the trend in prediction error of the rolling load in actual passes is not always constant for different passes even for the same stock. For example, often the error indicator CP of rolling load prediction in an actual pass found by formula (1) is multiplied with a gain α to flatten the trend in prediction error of the rolling load so as to set the learning coefficient CF for rolling force prediction at the predicted pass.
At this time, if making the gain α excessively large, the prediction error will tend to easily disperse, while if making the gain α excessively small, the prediction error of the rolling load will be harder to converge. To stably raise the precision of rolling load prediction by the present art, it is essential to set a suitable gain α.
Therefore, for example, Japanese Patent Publication (A) No. 50-108150 discloses the art of setting the learning coefficient CF of rolling force prediction at a predicted pass at which time, when the prediction error of the rolling load at the actual pass would be near the average value of past results, increasing the gain α multiplied with the prediction error of the rolling load at the actual pass and, when not, setting said gain α small so as to improve the precision of the rolling load prediction.
However, in general, the prediction error of the rolling load at an actual pass is distributed over a wide range, so with the method of adjusting the gain α to be multiplied with the error of the rolling load prediction in an actual pass in accordance with the error from the average value of the past results of the prediction error of the rolling load at an actual pass so as to set the learning coefficient CF of the rolling force prediction at the predicted pass, it is difficult to stably raise the precision of the rolling load prediction.
Japanese Patent Publication (A) No. 2000-126809 discloses the art of expressing the prediction error of the rolling load by a weighted sum of the prediction error of the friction coefficient and the prediction error of the material deformation resistance and correcting the respective weighting coefficients at each pass so as to thereby improve the prediction precision of the rolling load.
Japanese Patent Publication (A) No. 1-133606 discloses the art of using weighting coefficients showing the degrees of effect of the different parameters of a rolling load prediction formula on the rolling load so as to determine the learning coefficient for rolling load prediction to thereby improve the precision of the rolling load prediction.
Japanese Patent Publication (A) No. 10-263640 discloses the art of separating the learning coefficient for rolling load prediction into a component for correction of error distinctive to the rolling material and a component for correction of error due to aging of the rolling mill to thereby improve the precision of the rolling load prediction.
In this way, in art for correcting the prediction error of the rolling load based on envisioned error factors, if the envisioned error factors match with the actual situation, the precision of the rolling load prediction can probably in principle be improved.
However, the error factors of the rolling load include various factors such as the surface conditions of the stock and rolling rolls, the temperature and deformation characteristics of the stock, the precision of setting the rolling conditions, etc. It is extremely difficult to logically extract and estimate error of this large number of influencing factors.
That is, in the past, in rolling, no learning method could be found using the prediction error of the rolling load at an actual pass to correct the predicted value of rolling load at subsequent rolling passes and thereby stably improve the precision of the rolling load prediction.